摘 要
随着通信技术的快速发展，信号处理在复杂电磁环境下的高效性和智能化需求日益凸显。传统信号处理方法在面对高维度、非线性及动态变化的通信场景时存在局限性，而人工智能技术的引入为解决这一问题提供了新思路。本研究旨在探索基于人工智能的通信信号处理技术，以提升信号检测、分类与解调的性能。通过结合深度学习模型和优化算法，提出了一种融合卷积神经网络与长短期记忆网络的混合架构，用于实现对复杂调制信号的精准识别。此外，研究还设计了一种自适应特征提取机制，能够根据信号特性动态调整处理策略，从而显著提高系统鲁棒性。实验结果表明，所提出的模型在低信噪比条件下仍能保持较高的识别准确率，相较于传统方法具有明显优势。
关键词：人工智能信号处理 深度学习模型 卷积神经网络
Abstract
With the rapid development of communication technology, the high efficiency and intelligent demand of signal processing in the complex electromagnetic environment are increasingly prominent. Traditional signal processing methods have limitations in the face of high-dimensional, non-linear and dynamically changing communication scenarios, and the introduction of artificial intelligence technology provides a new way to solve this problem. This study aims to explore AI-based communication signal processing technologies to improve the performance of signal detection, classification and demodulation. By combining deep learning model and optimization algorithm, we propose a hybrid architecture integrating convolutional neural network and long-and short-term memory network, for the accurate recognition of complex modulated signals. Moreover, an adaptive feature extraction mechanism is designed that can dynamically adjust the processing strategy according to the signal characteristics, thus significantly improving the system robustness. The experimental results show that the proposed model can maintain high recognition accuracy under low signal to noise ratio, which has obvious advantages over the conventional methods.
Keyword:Artificial Intelligence Signal Processing Deep Learning Model Convolutional Neural Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
2人工智能在通信信号处理中的基础理论 2
2.1通信信号处理的基本概念 2
2.2人工智能技术的核心原理 3
2.3数据驱动的信号处理模型构建 3
2.4传统方法与 4
2.5基础理论对实际应用的指导 4
3基于人工智能的信号检测与分类技术 4
3.1信号检测的关键挑战 5
3.2深度学习在信号分类中的应用 5
3.3特征提取与降维技术优化 5
3.4异常信号识别算法设计 6
3.5实验验证与性能评估 6
4人工智能驱动的通信信号调制与解调 7
4.1调制解调技术的发展趋势 7
4.3自适应调制解调系统设计 8
4.4复杂环境下的信号处理策略 8
4.5性能提升的技术路径探讨 8
结论 9
参考文献 10
致谢 11
随着通信技术的快速发展，信号处理在复杂电磁环境下的高效性和智能化需求日益凸显。传统信号处理方法在面对高维度、非线性及动态变化的通信场景时存在局限性，而人工智能技术的引入为解决这一问题提供了新思路。本研究旨在探索基于人工智能的通信信号处理技术，以提升信号检测、分类与解调的性能。通过结合深度学习模型和优化算法，提出了一种融合卷积神经网络与长短期记忆网络的混合架构，用于实现对复杂调制信号的精准识别。此外，研究还设计了一种自适应特征提取机制，能够根据信号特性动态调整处理策略，从而显著提高系统鲁棒性。实验结果表明，所提出的模型在低信噪比条件下仍能保持较高的识别准确率，相较于传统方法具有明显优势。
关键词：人工智能信号处理 深度学习模型 卷积神经网络
Abstract
With the rapid development of communication technology, the high efficiency and intelligent demand of signal processing in the complex electromagnetic environment are increasingly prominent. Traditional signal processing methods have limitations in the face of high-dimensional, non-linear and dynamically changing communication scenarios, and the introduction of artificial intelligence technology provides a new way to solve this problem. This study aims to explore AI-based communication signal processing technologies to improve the performance of signal detection, classification and demodulation. By combining deep learning model and optimization algorithm, we propose a hybrid architecture integrating convolutional neural network and long-and short-term memory network, for the accurate recognition of complex modulated signals. Moreover, an adaptive feature extraction mechanism is designed that can dynamically adjust the processing strategy according to the signal characteristics, thus significantly improving the system robustness. The experimental results show that the proposed model can maintain high recognition accuracy under low signal to noise ratio, which has obvious advantages over the conventional methods.
Keyword:Artificial Intelligence Signal Processing Deep Learning Model Convolutional Neural Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
2人工智能在通信信号处理中的基础理论 2
2.1通信信号处理的基本概念 2
2.2人工智能技术的核心原理 3
2.3数据驱动的信号处理模型构建 3
2.4传统方法与 4
2.5基础理论对实际应用的指导 4
3基于人工智能的信号检测与分类技术 4
3.1信号检测的关键挑战 5
3.2深度学习在信号分类中的应用 5
3.3特征提取与降维技术优化 5
3.4异常信号识别算法设计 6
3.5实验验证与性能评估 6
4人工智能驱动的通信信号调制与解调 7
4.1调制解调技术的发展趋势 7
4.3自适应调制解调系统设计 8
4.4复杂环境下的信号处理策略 8
4.5性能提升的技术路径探讨 8
结论 9
参考文献 10
致谢 11
